Overview

Dataset statistics

Number of variables18
Number of observations12621960
Missing cells15275736
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 GiB
Average record size in memory145.0 B

Variable types

Text1
Numeric7
Categorical8
Boolean2

Alerts

Category is highly overall correlated with Exclude and 2 other fieldsHigh correlation
Exclude is highly overall correlated with Category and 3 other fieldsHigh correlation
Motif is highly overall correlated with Moving_Quickly and 3 other fieldsHigh correlation
Moving_Quickly is highly overall correlated with Exclude and 3 other fieldsHigh correlation
Predominant_Behavior is highly overall correlated with Category and 4 other fieldsHigh correlation
Secondary_Descriptor is highly overall correlated with Category and 4 other fieldsHigh correlation
centroid_x is highly overall correlated with ratHigh correlation
centroid_y is highly overall correlated with ratHigh correlation
distance is highly overall correlated with speedHigh correlation
group is highly overall correlated with rat_idHigh correlation
motif_hmm_650 is highly overall correlated with MotifHigh correlation
rat is highly overall correlated with centroid_x and 2 other fieldsHigh correlation
rat_id is highly overall correlated with group and 1 other fieldsHigh correlation
speed is highly overall correlated with distanceHigh correlation
in_center is highly imbalanced (66.6%)Imbalance
Exclude is highly imbalanced (53.1%)Imbalance
Moving_Quickly has 5166976 (40.9%) missing valuesMissing
Predominant_Behavior has 909134 (7.2%) missing valuesMissing
Secondary_Descriptor has 8290492 (65.7%) missing valuesMissing
Category has 909134 (7.2%) missing valuesMissing
rat is uniformly distributedUniform
motif_hmm_650 has 415439 (3.3%) zerosZeros
Motif has 415439 (3.3%) zerosZeros

Reproduction

Analysis started2024-01-30 01:50:21.784797
Analysis finished2024-01-30 02:17:10.082859
Duration26 minutes and 48.3 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct468
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.6 MiB
2024-01-29T20:17:10.237859image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length60
Median length57
Mean length57.692308
Min length57

Characters and Unicode

Total characters728190000
Distinct characters51
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat1
2nd row22-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat1
3rd row22-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat1
4th row22-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat1
5th row22-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat1
ValueCountFrequency (%)
22-01-10_baseline_1_djl_tabb_cropped_crf0_0min_to_15min_rat1 26970
 
0.2%
22-01-10_baseline_1_djl_tgbh_cropped_crf0_0min_to_15min_rat3 26970
 
0.2%
22-01-10_baseline_1_djl_tabb_cropped_crf0_0min_to_15min_rat3 26970
 
0.2%
22-01-10_baseline_1_djl_tabb_cropped_crf0_0min_to_15min_rat4 26970
 
0.2%
22-01-10_baseline_1_djl_tcbd_cropped_crf0_0min_to_15min_rat1 26970
 
0.2%
22-01-10_baseline_1_djl_tcbd_cropped_crf0_0min_to_15min_rat2 26970
 
0.2%
22-01-10_baseline_1_djl_tcbd_cropped_crf0_0min_to_15min_rat3 26970
 
0.2%
22-01-10_baseline_1_djl_tcbd_cropped_crf0_0min_to_15min_rat4 26970
 
0.2%
22-01-10_baseline_1_djl_tebf_cropped_crf0_0min_to_15min_rat1 26970
 
0.2%
22-01-10_baseline_1_djl_tebf_cropped_crf0_0min_to_15min_rat2 26970
 
0.2%
Other values (458) 12352260
97.9%
2024-01-29T20:17:10.508357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 126219600
17.3%
0 48546000
 
6.7%
2 37137690
 
5.1%
e 35924040
 
4.9%
1 33254010
 
4.6%
i 27833040
 
3.8%
n 27833040
 
3.8%
t 26214840
 
3.6%
R 26214840
 
3.6%
o 25243920
 
3.5%
Other values (41) 313768980
43.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 264737520
36.4%
Decimal Number 159554520
21.9%
Uppercase Letter 152434440
20.9%
Connector Punctuation 126219600
17.3%
Dash Punctuation 25243920
 
3.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 26214840
17.2%
B 16182000
10.6%
T 14671680
9.6%
D 14563800
9.6%
F 13700760
9.0%
J 13592880
8.9%
L 13592880
8.9%
C 13592880
8.9%
W 10140720
 
6.7%
K 1186680
 
0.8%
Other values (14) 14995320
9.8%
Lowercase Letter
ValueCountFrequency (%)
e 35924040
13.6%
i 27833040
10.5%
n 27833040
10.5%
t 26214840
9.9%
o 25243920
9.5%
p 25243920
9.5%
m 25243920
9.5%
a 15211080
5.7%
r 14563800
5.5%
c 12621960
 
4.8%
Other values (6) 28803960
10.9%
Decimal Number
ValueCountFrequency (%)
0 48546000
30.4%
2 37137690
23.3%
1 33254010
20.8%
5 17152920
 
10.8%
4 8981010
 
5.6%
3 8010090
 
5.0%
6 3236400
 
2.0%
8 2589120
 
1.6%
9 647280
 
0.4%
Connector Punctuation
ValueCountFrequency (%)
_ 126219600
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25243920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 417171960
57.3%
Common 311018040
42.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35924040
 
8.6%
i 27833040
 
6.7%
n 27833040
 
6.7%
t 26214840
 
6.3%
R 26214840
 
6.3%
o 25243920
 
6.1%
p 25243920
 
6.1%
m 25243920
 
6.1%
B 16182000
 
3.9%
a 15211080
 
3.6%
Other values (30) 166027320
39.8%
Common
ValueCountFrequency (%)
_ 126219600
40.6%
0 48546000
 
15.6%
2 37137690
 
11.9%
1 33254010
 
10.7%
- 25243920
 
8.1%
5 17152920
 
5.5%
4 8981010
 
2.9%
3 8010090
 
2.6%
6 3236400
 
1.0%
8 2589120
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 728190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 126219600
17.3%
0 48546000
 
6.7%
2 37137690
 
5.1%
e 35924040
 
4.9%
1 33254010
 
4.6%
i 27833040
 
3.8%
n 27833040
 
3.8%
t 26214840
 
3.6%
R 26214840
 
3.6%
o 25243920
 
3.5%
Other values (41) 313768980
43.1%

frame
Real number (ℝ)

Distinct26970
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13484.5
Minimum0
Maximum26969
Zeros468
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size192.6 MiB
2024-01-29T20:17:10.975358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1348
Q16742
median13484.5
Q320227
95-th percentile25621
Maximum26969
Range26969
Interquartile range (IQR)13485

Descriptive statistics

Standard deviation7785.5687
Coefficient of variation (CV)0.5773717
Kurtosis-1.2
Mean13484.5
Median Absolute Deviation (MAD)6742.5
Skewness0
Sum1.7020082 × 1011
Variance60615080
MonotonicityNot monotonic
2024-01-29T20:17:11.089357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 468
 
< 0.1%
17963 468
 
< 0.1%
17987 468
 
< 0.1%
17986 468
 
< 0.1%
17985 468
 
< 0.1%
17984 468
 
< 0.1%
17983 468
 
< 0.1%
17982 468
 
< 0.1%
17981 468
 
< 0.1%
17980 468
 
< 0.1%
Other values (26960) 12617280
> 99.9%
ValueCountFrequency (%)
0 468
< 0.1%
1 468
< 0.1%
2 468
< 0.1%
3 468
< 0.1%
4 468
< 0.1%
5 468
< 0.1%
6 468
< 0.1%
7 468
< 0.1%
8 468
< 0.1%
9 468
< 0.1%
ValueCountFrequency (%)
26969 468
< 0.1%
26968 468
< 0.1%
26967 468
< 0.1%
26966 468
< 0.1%
26965 468
< 0.1%
26964 468
< 0.1%
26963 468
< 0.1%
26962 468
< 0.1%
26961 468
< 0.1%
26960 468
< 0.1%

motif_hmm_650
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.139519
Minimum0
Maximum39
Zeros415439
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size192.6 MiB
2024-01-29T20:17:11.193357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median18
Q327
95-th percentile36
Maximum39
Range39
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.058083
Coefficient of variation (CV)0.60961283
Kurtosis-1.0404659
Mean18.139519
Median Absolute Deviation (MAD)9
Skewness0.091913321
Sum2.2895628 × 108
Variance122.28121
MonotonicityNot monotonic
2024-01-29T20:17:11.304357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20 549827
 
4.4%
22 505043
 
4.0%
23 500697
 
4.0%
2 494792
 
3.9%
15 486322
 
3.9%
28 481264
 
3.8%
25 470826
 
3.7%
8 463476
 
3.7%
1 446186
 
3.5%
0 415439
 
3.3%
Other values (30) 7808088
61.9%
ValueCountFrequency (%)
0 415439
3.3%
1 446186
3.5%
2 494792
3.9%
3 185081
 
1.5%
4 241433
1.9%
5 275160
2.2%
6 228702
1.8%
7 296887
2.4%
8 463476
3.7%
9 219514
1.7%
ValueCountFrequency (%)
39 131031
 
1.0%
38 264651
2.1%
37 198652
1.6%
36 371074
2.9%
35 262182
2.1%
34 267704
2.1%
33 229111
1.8%
32 305242
2.4%
31 233
 
< 0.1%
30 246862
2.0%

centroid_x
Real number (ℝ)

HIGH CORRELATION 

Distinct8477894
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.58075
Minimum21.309204
Maximum630.2107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.6 MiB
2024-01-29T20:17:11.430356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum21.309204
5-th percentile58.31167
Q180.336666
median334.48778
Q3483.84117
95-th percentile592.8322
Maximum630.2107
Range608.9015
Interquartile range (IQR)403.5045

Descriptive statistics

Standard deviation201.53799
Coefficient of variation (CV)0.67273344
Kurtosis-1.5410139
Mean299.58075
Median Absolute Deviation (MAD)230.52156
Skewness0.1169313
Sum3.7812962 × 109
Variance40617.56
MonotonicityNot monotonic
2024-01-29T20:17:11.541358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
592.5728 16
 
< 0.1%
582.30963 15
 
< 0.1%
575.4343 15
 
< 0.1%
581.4392 15
 
< 0.1%
593.27814 14
 
< 0.1%
587.7342 14
 
< 0.1%
580.50073 14
 
< 0.1%
594.2528 14
 
< 0.1%
589.75006 14
 
< 0.1%
593.4699 14
 
< 0.1%
Other values (8477884) 12621815
> 99.9%
ValueCountFrequency (%)
21.309204 1
< 0.1%
21.342188 1
< 0.1%
21.629887 1
< 0.1%
21.700506 1
< 0.1%
22.305332 1
< 0.1%
22.331964 1
< 0.1%
22.955336 1
< 0.1%
23.167027 1
< 0.1%
23.35288 1
< 0.1%
23.456802 1
< 0.1%
ValueCountFrequency (%)
630.2107 1
< 0.1%
630.0491 1
< 0.1%
628.40826 1
< 0.1%
627.9597 1
< 0.1%
626.09924 1
< 0.1%
625.4818 1
< 0.1%
625.33374 1
< 0.1%
625.3282 1
< 0.1%
625.0347 1
< 0.1%
625.0173 1
< 0.1%

centroid_y
Real number (ℝ)

HIGH CORRELATION 

Distinct8011955
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345.26661
Minimum33.870197
Maximum625.66895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.6 MiB
2024-01-29T20:17:11.679356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum33.870197
5-th percentile68.470696
Q1222.9681
median328.47266
Q3531.19366
95-th percentile589.77344
Maximum625.66895
Range591.79875
Interquartile range (IQR)308.22556

Descriptive statistics

Standard deviation174.50349
Coefficient of variation (CV)0.50541665
Kurtosis-1.2538612
Mean345.26661
Median Absolute Deviation (MAD)151.54609
Skewness-0.028541104
Sum4.3579414 × 109
Variance30451.47
MonotonicityNot monotonic
2024-01-29T20:17:11.782357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
577.58014 17
 
< 0.1%
577.15295 17
 
< 0.1%
578.5644 16
 
< 0.1%
576.8431 16
 
< 0.1%
579.9652 15
 
< 0.1%
570.84534 15
 
< 0.1%
586.84174 15
 
< 0.1%
586.8863 15
 
< 0.1%
579.7055 15
 
< 0.1%
586.18744 15
 
< 0.1%
Other values (8011945) 12621804
> 99.9%
ValueCountFrequency (%)
33.870197 1
< 0.1%
33.871677 1
< 0.1%
33.955666 1
< 0.1%
33.96398 1
< 0.1%
34.07011 1
< 0.1%
34.398083 1
< 0.1%
34.50547 1
< 0.1%
34.568867 1
< 0.1%
34.629757 1
< 0.1%
34.79434 1
< 0.1%
ValueCountFrequency (%)
625.66895 1
< 0.1%
625.0903 1
< 0.1%
624.2235 1
< 0.1%
622.7253 1
< 0.1%
620.85626 1
< 0.1%
620.7995 1
< 0.1%
620.5762 1
< 0.1%
620.1259 1
< 0.1%
620.05914 1
< 0.1%
620.01697 1
< 0.1%

in_center
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.6 MiB
0.0
11842773 
1.0
 
779187

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters37865880
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11842773
93.8%
1.0 779187
 
6.2%

Length

2024-01-29T20:17:11.880358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T20:17:11.962358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11842773
93.8%
1.0 779187
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 24464733
64.6%
. 12621960
33.3%
1 779187
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25243920
66.7%
Other Punctuation 12621960
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24464733
96.9%
1 779187
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 12621960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37865880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24464733
64.6%
. 12621960
33.3%
1 779187
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37865880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24464733
64.6%
. 12621960
33.3%
1 779187
 
2.1%

distance
Real number (ℝ)

HIGH CORRELATION 

Distinct11346129
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1773651
Minimum7.142701 × 10-6
Maximum11.729444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.6 MiB
2024-01-29T20:17:12.089358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7.142701 × 10-6
5-th percentile0.0073130835
Q10.028875574
median0.074520355
Q30.18493218
95-th percentile0.78468028
Maximum11.729444
Range11.729437
Interquartile range (IQR)0.1560566

Descriptive statistics

Standard deviation0.28175685
Coefficient of variation (CV)1.5885699
Kurtosis14.088951
Mean0.1773651
Median Absolute Deviation (MAD)0.055968503
Skewness3.2648956
Sum2238695.2
Variance0.079386924
MonotonicityNot monotonic
2024-01-29T20:17:12.202358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.008894745 182
 
< 0.1%
0.04950115 132
 
< 0.1%
0.013804477 82
 
< 0.1%
0.05827828 66
 
< 0.1%
0.008747817 53
 
< 0.1%
0.13631444 50
 
< 0.1%
0.06681175 49
 
< 0.1%
0.03953116 48
 
< 0.1%
0.13740107 40
 
< 0.1%
0.084319 36
 
< 0.1%
Other values (11346119) 12621222
> 99.9%
ValueCountFrequency (%)
7.142701 × 10-61
< 0.1%
8.833231 × 10-61
< 0.1%
1.5805097 × 10-51
< 0.1%
1.907038 × 10-51
< 0.1%
1.9821771 × 10-51
< 0.1%
2.0852629 × 10-51
< 0.1%
2.1335338 × 10-51
< 0.1%
2.1438038 × 10-51
< 0.1%
2.3450972 × 10-51
< 0.1%
2.4067731 × 10-51
< 0.1%
ValueCountFrequency (%)
11.7294445 1
< 0.1%
11.706009 1
< 0.1%
11.17524 1
< 0.1%
9.56251 1
< 0.1%
9.482468 1
< 0.1%
9.046187 1
< 0.1%
8.752678 1
< 0.1%
8.302079 1
< 0.1%
8.046537 1
< 0.1%
8.038197 1
< 0.1%

speed
Real number (ℝ)

HIGH CORRELATION 

Distinct11267460
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3215854
Minimum0.00796373
Maximum116.38814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size192.6 MiB
2024-01-29T20:17:12.347355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.00796373
5-th percentile0.29368799
Q10.96012717
median2.3152779
Q35.5152233
95-th percentile23.273718
Maximum116.38814
Range116.38018
Interquartile range (IQR)4.5550961

Descriptive statistics

Standard deviation8.2763471
Coefficient of variation (CV)1.5552409
Kurtosis12.126764
Mean5.3215854
Median Absolute Deviation (MAD)1.6642217
Skewness3.1918233
Sum67168838
Variance68.497921
MonotonicityNot monotonic
2024-01-29T20:17:12.459358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26684237 178
 
< 0.1%
1.4850345 128
 
< 0.1%
0.4141343 79
 
< 0.1%
1.7483484 63
 
< 0.1%
0.2624345 49
 
< 0.1%
2.0043526 46
 
< 0.1%
4.089433 46
 
< 0.1%
1.1859348 45
 
< 0.1%
4.122032 36
 
< 0.1%
2.52957 32
 
< 0.1%
Other values (11267450) 12621258
> 99.9%
ValueCountFrequency (%)
0.00796373 1
< 0.1%
0.008488682 1
< 0.1%
0.008681333 1
< 0.1%
0.009028143 1
< 0.1%
0.010097986 1
< 0.1%
0.010422492 1
< 0.1%
0.01080666 1
< 0.1%
0.010972135 1
< 0.1%
0.011098506 1
< 0.1%
0.011235485 1
< 0.1%
ValueCountFrequency (%)
116.38814 1
< 0.1%
114.620026 1
< 0.1%
112.355034 1
< 0.1%
110.52341 1
< 0.1%
108.23665 1
< 0.1%
105.33802 1
< 0.1%
101.9892 1
< 0.1%
101.62444 1
< 0.1%
100.751114 1
< 0.1%
100.62463 1
< 0.1%

rat
Categorical

HIGH CORRELATION  UNIFORM 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.6 MiB
Rat1
3155490 
Rat2
3155490 
Rat3
3155490 
Rat4
3155490 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters50487840
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRat1
2nd rowRat1
3rd rowRat1
4th rowRat1
5th rowRat1

Common Values

ValueCountFrequency (%)
Rat1 3155490
25.0%
Rat2 3155490
25.0%
Rat3 3155490
25.0%
Rat4 3155490
25.0%

Length

2024-01-29T20:17:12.564358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T20:17:12.649359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
rat1 3155490
25.0%
rat2 3155490
25.0%
rat3 3155490
25.0%
rat4 3155490
25.0%

Most occurring characters

ValueCountFrequency (%)
R 12621960
25.0%
a 12621960
25.0%
t 12621960
25.0%
1 3155490
 
6.2%
2 3155490
 
6.2%
3 3155490
 
6.2%
4 3155490
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25243920
50.0%
Uppercase Letter 12621960
25.0%
Decimal Number 12621960
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3155490
25.0%
2 3155490
25.0%
3 3155490
25.0%
4 3155490
25.0%
Lowercase Letter
ValueCountFrequency (%)
a 12621960
50.0%
t 12621960
50.0%
Uppercase Letter
ValueCountFrequency (%)
R 12621960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37865880
75.0%
Common 12621960
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3155490
25.0%
2 3155490
25.0%
3 3155490
25.0%
4 3155490
25.0%
Latin
ValueCountFrequency (%)
R 12621960
33.3%
a 12621960
33.3%
t 12621960
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50487840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 12621960
25.0%
a 12621960
25.0%
t 12621960
25.0%
1 3155490
 
6.2%
2 3155490
 
6.2%
3 3155490
 
6.2%
4 3155490
 
6.2%

rat_id
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.6 MiB
A1
 
269700
Y1
 
269700
M1
 
269700
K1
 
269700
F2
 
269700
Other values (43)
11273460 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters25243920
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA1
2nd rowA1
3rd rowA1
4th rowA1
5th rowA1

Common Values

ValueCountFrequency (%)
A1 269700
 
2.1%
Y1 269700
 
2.1%
M1 269700
 
2.1%
K1 269700
 
2.1%
F2 269700
 
2.1%
P2 269700
 
2.1%
D2 269700
 
2.1%
E1 269700
 
2.1%
E2 269700
 
2.1%
F1 269700
 
2.1%
Other values (38) 9924960
78.6%

Length

2024-01-29T20:17:12.741358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a1 269700
 
2.1%
x2 269700
 
2.1%
l2 269700
 
2.1%
a2 269700
 
2.1%
x1 269700
 
2.1%
y1 269700
 
2.1%
n2 269700
 
2.1%
o1 269700
 
2.1%
o2 269700
 
2.1%
m2 269700
 
2.1%
Other values (38) 9924960
78.6%

Most occurring characters

ValueCountFrequency (%)
2 6310980
25.0%
1 6310980
25.0%
A 539400
 
2.1%
I 539400
 
2.1%
W 539400
 
2.1%
T 539400
 
2.1%
S 539400
 
2.1%
O 539400
 
2.1%
N 539400
 
2.1%
X 539400
 
2.1%
Other values (16) 8306760
32.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12621960
50.0%
Uppercase Letter 12621960
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 539400
 
4.3%
I 539400
 
4.3%
W 539400
 
4.3%
T 539400
 
4.3%
S 539400
 
4.3%
O 539400
 
4.3%
N 539400
 
4.3%
X 539400
 
4.3%
H 539400
 
4.3%
L 539400
 
4.3%
Other values (14) 7227960
57.3%
Decimal Number
ValueCountFrequency (%)
2 6310980
50.0%
1 6310980
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12621960
50.0%
Latin 12621960
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 539400
 
4.3%
I 539400
 
4.3%
W 539400
 
4.3%
T 539400
 
4.3%
S 539400
 
4.3%
O 539400
 
4.3%
N 539400
 
4.3%
X 539400
 
4.3%
H 539400
 
4.3%
L 539400
 
4.3%
Other values (14) 7227960
57.3%
Common
ValueCountFrequency (%)
2 6310980
50.0%
1 6310980
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25243920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6310980
25.0%
1 6310980
25.0%
A 539400
 
2.1%
I 539400
 
2.1%
W 539400
 
2.1%
T 539400
 
2.1%
S 539400
 
2.1%
O 539400
 
2.1%
N 539400
 
2.1%
X 539400
 
2.1%
Other values (16) 8306760
32.9%

group
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.6 MiB
Sham
3236400 
Injured
3236400 
Treated
3236400 
ABX
2912760 

Length

Max length7
Median length7
Mean length5.3076923
Min length3

Characters and Unicode

Total characters66993480
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSham
2nd rowSham
3rd rowSham
4th rowSham
5th rowSham

Common Values

ValueCountFrequency (%)
Sham 3236400
25.6%
Injured 3236400
25.6%
Treated 3236400
25.6%
ABX 2912760
23.1%

Length

2024-01-29T20:17:12.842357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T20:17:12.934358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
sham 3236400
25.6%
injured 3236400
25.6%
treated 3236400
25.6%
abx 2912760
23.1%

Most occurring characters

ValueCountFrequency (%)
e 9709200
14.5%
a 6472800
 
9.7%
r 6472800
 
9.7%
d 6472800
 
9.7%
S 3236400
 
4.8%
h 3236400
 
4.8%
m 3236400
 
4.8%
I 3236400
 
4.8%
n 3236400
 
4.8%
j 3236400
 
4.8%
Other values (6) 18447480
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48546000
72.5%
Uppercase Letter 18447480
 
27.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9709200
20.0%
a 6472800
13.3%
r 6472800
13.3%
d 6472800
13.3%
h 3236400
 
6.7%
m 3236400
 
6.7%
n 3236400
 
6.7%
j 3236400
 
6.7%
u 3236400
 
6.7%
t 3236400
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
S 3236400
17.5%
I 3236400
17.5%
T 3236400
17.5%
A 2912760
15.8%
B 2912760
15.8%
X 2912760
15.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 66993480
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9709200
14.5%
a 6472800
 
9.7%
r 6472800
 
9.7%
d 6472800
 
9.7%
S 3236400
 
4.8%
h 3236400
 
4.8%
m 3236400
 
4.8%
I 3236400
 
4.8%
n 3236400
 
4.8%
j 3236400
 
4.8%
Other values (6) 18447480
27.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66993480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9709200
14.5%
a 6472800
 
9.7%
r 6472800
 
9.7%
d 6472800
 
9.7%
S 3236400
 
4.8%
h 3236400
 
4.8%
m 3236400
 
4.8%
I 3236400
 
4.8%
n 3236400
 
4.8%
j 3236400
 
4.8%
Other values (6) 18447480
27.5%

time_point
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size192.6 MiB
Baseline_1
1294560 
Baseline_2
1294560 
Week_02
1294560 
Week_04
1294560 
Week_06
1294560 
Other values (5)
6149160 

Length

Max length10
Median length7
Mean length7.6923077
Min length7

Characters and Unicode

Total characters97092000
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBaseline_1
2nd rowBaseline_1
3rd rowBaseline_1
4th rowBaseline_1
5th rowBaseline_1

Common Values

ValueCountFrequency (%)
Baseline_1 1294560
10.3%
Baseline_2 1294560
10.3%
Week_02 1294560
10.3%
Week_04 1294560
10.3%
Week_06 1294560
10.3%
Week_08 1294560
10.3%
Week_11 1294560
10.3%
Week_13 1294560
10.3%
Week_15 1294560
10.3%
Drug_Trt 970920
7.7%

Length

2024-01-29T20:17:13.030358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T20:17:13.131358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
baseline_1 1294560
10.3%
baseline_2 1294560
10.3%
week_02 1294560
10.3%
week_04 1294560
10.3%
week_06 1294560
10.3%
week_08 1294560
10.3%
week_11 1294560
10.3%
week_13 1294560
10.3%
week_15 1294560
10.3%
drug_trt 970920
7.7%

Most occurring characters

ValueCountFrequency (%)
e 23302080
24.0%
_ 12621960
13.0%
k 9061920
 
9.3%
W 9061920
 
9.3%
1 6472800
 
6.7%
0 5178240
 
5.3%
a 2589120
 
2.7%
2 2589120
 
2.7%
B 2589120
 
2.7%
n 2589120
 
2.7%
Other values (14) 21036600
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50164200
51.7%
Decimal Number 20712960
21.3%
Uppercase Letter 13592880
 
14.0%
Connector Punctuation 12621960
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 23302080
46.5%
k 9061920
 
18.1%
a 2589120
 
5.2%
n 2589120
 
5.2%
i 2589120
 
5.2%
l 2589120
 
5.2%
s 2589120
 
5.2%
r 1941840
 
3.9%
u 970920
 
1.9%
g 970920
 
1.9%
Decimal Number
ValueCountFrequency (%)
1 6472800
31.2%
0 5178240
25.0%
2 2589120
 
12.5%
4 1294560
 
6.2%
6 1294560
 
6.2%
8 1294560
 
6.2%
3 1294560
 
6.2%
5 1294560
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
W 9061920
66.7%
B 2589120
 
19.0%
D 970920
 
7.1%
T 970920
 
7.1%
Connector Punctuation
ValueCountFrequency (%)
_ 12621960
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63757080
65.7%
Common 33334920
34.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 23302080
36.5%
k 9061920
 
14.2%
W 9061920
 
14.2%
a 2589120
 
4.1%
B 2589120
 
4.1%
n 2589120
 
4.1%
i 2589120
 
4.1%
l 2589120
 
4.1%
s 2589120
 
4.1%
r 1941840
 
3.0%
Other values (5) 4854600
 
7.6%
Common
ValueCountFrequency (%)
_ 12621960
37.9%
1 6472800
19.4%
0 5178240
15.5%
2 2589120
 
7.8%
4 1294560
 
3.9%
6 1294560
 
3.9%
8 1294560
 
3.9%
3 1294560
 
3.9%
5 1294560
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97092000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 23302080
24.0%
_ 12621960
13.0%
k 9061920
 
9.3%
W 9061920
 
9.3%
1 6472800
 
6.7%
0 5178240
 
5.3%
a 2589120
 
2.7%
2 2589120
 
2.7%
B 2589120
 
2.7%
n 2589120
 
2.7%
Other values (14) 21036600
21.7%

Motif
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.139519
Minimum0
Maximum39
Zeros415439
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size192.6 MiB
2024-01-29T20:17:13.258357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median18
Q327
95-th percentile36
Maximum39
Range39
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.058083
Coefficient of variation (CV)0.60961283
Kurtosis-1.0404659
Mean18.139519
Median Absolute Deviation (MAD)9
Skewness0.091913321
Sum2.2895628 × 108
Variance122.28121
MonotonicityNot monotonic
2024-01-29T20:17:13.368357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20 549827
 
4.4%
22 505043
 
4.0%
23 500697
 
4.0%
2 494792
 
3.9%
15 486322
 
3.9%
28 481264
 
3.8%
25 470826
 
3.7%
8 463476
 
3.7%
1 446186
 
3.5%
0 415439
 
3.3%
Other values (30) 7808088
61.9%
ValueCountFrequency (%)
0 415439
3.3%
1 446186
3.5%
2 494792
3.9%
3 185081
 
1.5%
4 241433
1.9%
5 275160
2.2%
6 228702
1.8%
7 296887
2.4%
8 463476
3.7%
9 219514
1.7%
ValueCountFrequency (%)
39 131031
 
1.0%
38 264651
2.1%
37 198652
1.6%
36 371074
2.9%
35 262182
2.1%
34 267704
2.1%
33 229111
1.8%
32 305242
2.4%
31 233
 
< 0.1%
30 246862
2.0%

Exclude
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size108.3 MiB
False
11360481 
True
1261479 
ValueCountFrequency (%)
False 11360481
90.0%
True 1261479
 
10.0%
2024-01-29T20:17:13.460358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Moving_Quickly
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing5166976
Missing (%)40.9%
Memory size192.6 MiB
False
4230312 
True
3224672 
(Missing)
5166976 
ValueCountFrequency (%)
False 4230312
33.5%
True 3224672
25.5%
(Missing) 5166976
40.9%
2024-01-29T20:17:13.533358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Predominant_Behavior
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing909134
Missing (%)7.2%
Memory size192.6 MiB
Sniffing
5184591 
Rearing
3060784 
Stationary
1464819 
Grooming
1168789 
Walking
728127 

Length

Max length15
Median length8
Mean length7.9898179
Min length7

Characters and Unicode

Total characters93583347
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRearing
2nd rowRearing
3rd rowRearing
4th rowRearing
5th rowRearing

Common Values

ValueCountFrequency (%)
Sniffing 5184591
41.1%
Rearing 3060784
24.2%
Stationary 1464819
 
11.6%
Grooming 1168789
 
9.3%
Walking 728127
 
5.8%
Mixed behaviors 105716
 
0.8%
(Missing) 909134
 
7.2%

Length

2024-01-29T20:17:13.631359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T20:17:13.725358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
sniffing 5184591
43.9%
rearing 3060784
25.9%
stationary 1464819
 
12.4%
grooming 1168789
 
9.9%
walking 728127
 
6.2%
mixed 105716
 
0.9%
behaviors 105716
 
0.9%

Most occurring characters

ValueCountFrequency (%)
i 17003133
18.2%
n 16791701
17.9%
f 10369182
11.1%
g 10142291
10.8%
a 6824265
7.3%
S 6649410
 
7.1%
r 5800108
 
6.2%
o 3908113
 
4.2%
e 3272216
 
3.5%
R 3060784
 
3.3%
Other values (15) 9762144
10.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81764805
87.4%
Uppercase Letter 11712826
 
12.5%
Space Separator 105716
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 17003133
20.8%
n 16791701
20.5%
f 10369182
12.7%
g 10142291
12.4%
a 6824265
8.3%
r 5800108
 
7.1%
o 3908113
 
4.8%
e 3272216
 
4.0%
t 2929638
 
3.6%
y 1464819
 
1.8%
Other values (9) 3259339
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
S 6649410
56.8%
R 3060784
26.1%
G 1168789
 
10.0%
W 728127
 
6.2%
M 105716
 
0.9%
Space Separator
ValueCountFrequency (%)
105716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93477631
99.9%
Common 105716
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 17003133
18.2%
n 16791701
18.0%
f 10369182
11.1%
g 10142291
10.8%
a 6824265
7.3%
S 6649410
 
7.1%
r 5800108
 
6.2%
o 3908113
 
4.2%
e 3272216
 
3.5%
R 3060784
 
3.3%
Other values (14) 9656428
10.3%
Common
ValueCountFrequency (%)
105716
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93583347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 17003133
18.2%
n 16791701
17.9%
f 10369182
11.1%
g 10142291
10.8%
a 6824265
7.3%
S 6649410
 
7.1%
r 5800108
 
6.2%
o 3908113
 
4.2%
e 3272216
 
3.5%
R 3060784
 
3.3%
Other values (15) 9762144
10.4%

Secondary_Descriptor
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing8290492
Missing (%)65.7%
Memory size192.6 MiB
Active
3249868 
Stationary
816949 
Quick
 
264651

Length

Max length10
Median length6
Mean length6.6933319
Min length5

Characters and Unicode

Total characters28991953
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowActive
2nd rowActive
3rd rowActive
4th rowActive
5th rowActive

Common Values

ValueCountFrequency (%)
Active 3249868
 
25.7%
Stationary 816949
 
6.5%
Quick 264651
 
2.1%
(Missing) 8290492
65.7%

Length

2024-01-29T20:17:13.830359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T20:17:13.911358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
active 3249868
75.0%
stationary 816949
 
18.9%
quick 264651
 
6.1%

Most occurring characters

ValueCountFrequency (%)
t 4883766
16.8%
i 4331468
14.9%
c 3514519
12.1%
A 3249868
11.2%
v 3249868
11.2%
e 3249868
11.2%
a 1633898
 
5.6%
S 816949
 
2.8%
o 816949
 
2.8%
n 816949
 
2.8%
Other values (5) 2427851
8.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24660485
85.1%
Uppercase Letter 4331468
 
14.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4883766
19.8%
i 4331468
17.6%
c 3514519
14.3%
v 3249868
13.2%
e 3249868
13.2%
a 1633898
 
6.6%
o 816949
 
3.3%
n 816949
 
3.3%
r 816949
 
3.3%
y 816949
 
3.3%
Other values (2) 529302
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
A 3249868
75.0%
S 816949
 
18.9%
Q 264651
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 28991953
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4883766
16.8%
i 4331468
14.9%
c 3514519
12.1%
A 3249868
11.2%
v 3249868
11.2%
e 3249868
11.2%
a 1633898
 
5.6%
S 816949
 
2.8%
o 816949
 
2.8%
n 816949
 
2.8%
Other values (5) 2427851
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28991953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 4883766
16.8%
i 4331468
14.9%
c 3514519
12.1%
A 3249868
11.2%
v 3249868
11.2%
e 3249868
11.2%
a 1633898
 
5.6%
S 816949
 
2.8%
o 816949
 
2.8%
n 816949
 
2.8%
Other values (5) 2427851
8.4%

Category
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing909134
Missing (%)7.2%
Memory size192.6 MiB
Exploratory
8510026 
Locomotor
1632265 
Resting
1464819 
Mixed
 
105716

Length

Max length11
Median length11
Mean length10.166887
Min length5

Characters and Unicode

Total characters119082984
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExploratory
2nd rowExploratory
3rd rowExploratory
4th rowExploratory
5th rowExploratory

Common Values

ValueCountFrequency (%)
Exploratory 8510026
67.4%
Locomotor 1632265
 
12.9%
Resting 1464819
 
11.6%
Mixed 105716
 
0.8%
(Missing) 909134
 
7.2%

Length

2024-01-29T20:17:14.004356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T20:17:14.092358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
exploratory 8510026
72.7%
locomotor 1632265
 
13.9%
resting 1464819
 
12.5%
mixed 105716
 
0.9%

Most occurring characters

ValueCountFrequency (%)
o 23549112
19.8%
r 18652317
15.7%
t 11607110
9.7%
x 8615742
 
7.2%
E 8510026
 
7.1%
p 8510026
 
7.1%
l 8510026
 
7.1%
a 8510026
 
7.1%
y 8510026
 
7.1%
m 1632265
 
1.4%
Other values (10) 12476308
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107370158
90.2%
Uppercase Letter 11712826
 
9.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 23549112
21.9%
r 18652317
17.4%
t 11607110
10.8%
x 8615742
 
8.0%
p 8510026
 
7.9%
l 8510026
 
7.9%
a 8510026
 
7.9%
y 8510026
 
7.9%
m 1632265
 
1.5%
c 1632265
 
1.5%
Other values (6) 7641243
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
E 8510026
72.7%
L 1632265
 
13.9%
R 1464819
 
12.5%
M 105716
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 119082984
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 23549112
19.8%
r 18652317
15.7%
t 11607110
9.7%
x 8615742
 
7.2%
E 8510026
 
7.1%
p 8510026
 
7.1%
l 8510026
 
7.1%
a 8510026
 
7.1%
y 8510026
 
7.1%
m 1632265
 
1.4%
Other values (10) 12476308
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119082984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 23549112
19.8%
r 18652317
15.7%
t 11607110
9.7%
x 8615742
 
7.2%
E 8510026
 
7.1%
p 8510026
 
7.1%
l 8510026
 
7.1%
a 8510026
 
7.1%
y 8510026
 
7.1%
m 1632265
 
1.4%
Other values (10) 12476308
10.5%

Interactions

2024-01-29T20:15:52.549868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:12.591874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:19.290868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:26.110869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:32.709871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:39.312868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:45.915870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:53.516873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:13.582871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:20.272868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:27.062870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:33.654870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:40.258870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:46.863871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:54.476869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:14.531869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:21.281869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:27.965868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:34.588870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:41.221871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:47.810871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:55.443869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:15.474873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:22.243869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:28.920872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:35.502869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:42.169868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:48.771870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:56.403870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:16.428870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:23.212871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:29.873872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:36.436869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:43.077871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:49.720868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:57.360870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:17.372871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:24.169868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:30.825868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:37.393872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:44.012870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:50.638870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:58.294868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:18.319869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:25.153875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:31.777868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:38.351868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:44.952868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-29T20:15:51.591868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-01-29T20:17:14.167358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CategoryExcludeMotifMoving_QuicklyPredominant_BehaviorSecondary_Descriptorcentroid_xcentroid_ydistanceframegroupin_centermotif_hmm_650ratrat_idspeedtime_point
Category1.0000.6220.1690.3580.9791.0000.0180.029-0.3490.1810.0310.0400.1690.0480.110-0.3750.055
Exclude0.6221.0000.1011.0000.6561.000-0.0070.036-0.122-0.0140.0190.0380.1010.0570.085-0.1340.060
Motif0.1690.1011.0000.6350.5050.8270.0140.017-0.1440.0610.0350.0771.0000.1000.088-0.1550.038
Moving_Quickly0.3581.0000.6351.0000.6240.5640.004-0.0880.424-0.2320.0260.0580.0550.0740.1260.4640.084
Predominant_Behavior0.9790.6560.5050.6241.0001.0000.090-0.0700.0180.0080.0470.093-0.0670.1040.1260.0200.060
Secondary_Descriptor1.0001.0000.8270.5641.0001.0000.004-0.015-0.3910.1670.0350.0460.3570.0770.157-0.4260.135
centroid_x0.018-0.0070.0140.0040.0900.0041.000-0.0010.077-0.0340.0470.3620.0140.5810.3560.0800.038
centroid_y0.0290.0360.017-0.088-0.070-0.015-0.0011.000-0.0430.0050.1340.2930.0170.5820.326-0.0450.030
distance-0.349-0.122-0.1440.4240.018-0.3910.077-0.0431.000-0.2480.0060.093-0.1440.0130.0210.9350.010
frame0.181-0.0140.061-0.2320.0080.167-0.0340.005-0.2481.0000.0000.0300.0610.0000.000-0.2610.000
group0.0310.0190.0350.0260.0470.0350.0470.1340.0060.0001.0000.0480.0100.1281.000-0.0110.091
in_center0.0400.0380.0770.0580.0930.0460.3620.2930.0930.0300.0481.000-0.0310.1180.2150.0650.053
motif_hmm_6500.1690.1011.0000.055-0.0670.3570.0140.017-0.1440.0610.010-0.0311.0000.1000.088-0.1550.038
rat0.0480.0570.1000.0740.1040.0770.5810.5820.0130.0000.1280.1180.1001.0000.942-0.0080.000
rat_id0.1100.0850.0880.1260.1260.1570.3560.3260.0210.0001.0000.2150.0880.9421.0000.0170.053
speed-0.375-0.134-0.1550.4640.020-0.4260.080-0.0450.935-0.261-0.0110.065-0.155-0.0080.0171.0000.018
time_point0.0550.0600.0380.0840.0600.1350.0380.0300.0100.0000.0910.0530.0380.0000.0530.0181.000

Missing values

2024-01-29T20:16:06.923869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T20:16:21.594870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

file_nameframemotif_hmm_650centroid_xcentroid_yin_centerdistancespeedratrat_idgrouptime_pointMotifExcludeMoving_QuicklyPredominant_BehaviorSecondary_DescriptorCategory
022-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat102873.74916281.147250.00.2370285.788066Rat1A1ShamBaseline_128FalseNaNRearingNaNExploratory
122-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat11073.97390279.875730.00.2776116.482201Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
222-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat12074.12506278.422820.00.3140637.303471Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
322-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat13074.03892276.728200.00.3648178.381433Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
422-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat14073.83466274.998960.00.3743729.407344Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
522-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat15073.48978273.127930.00.40904610.439452Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
622-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat16073.04165270.889980.00.49071211.718060Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
722-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat17072.51922268.962980.00.42926012.409242Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
822-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat18071.96824267.162840.00.40475512.648870Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
922-01-10_Baseline_1_DJL_TABB_cropped_CRF0_0min_to_15min_Rat19071.44137265.380580.00.39957912.800114Rat1A1ShamBaseline_10FalseFalseRearingNaNExploratory
file_nameframemotif_hmm_650centroid_xcentroid_yin_centerdistancespeedratrat_idgrouptime_pointMotifExcludeMoving_QuicklyPredominant_BehaviorSecondary_DescriptorCategory
1262195022-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696011376.16336375.054930.00.0729702.255560Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195122-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696111376.07925375.340030.00.0639132.191613Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195222-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696211376.03363375.572940.00.0510272.051072Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195322-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696311376.04352375.745500.00.0371631.813118Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195422-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696411376.11203375.853400.00.0274761.515297Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195522-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696511376.22357375.895780.00.0256571.231416Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195622-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696611376.33826375.875060.00.0250570.998283Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195722-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696711376.38602375.796050.00.0198450.811193Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195822-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696811376.26016375.665300.00.0390140.822301Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory
1262195922-05-05_Drug_Trt_DJL_TXBY_cropped_CRF0_0min_to_15min_Rat42696911375.81070375.490000.00.1037291.279816Rat4Y2TreatedDrug_Trt11FalseFalseSniffingActiveExploratory